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Child Abuse

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Development and Validation of a Natural Language Processing Tool to Identify Injuries in Infants Associated With Abuse.

Academic pediatrics
OBJECTIVES: Medically minor but clinically important findings associated with physical child abuse, such as bruises in pre-mobile infants, may be identified by frontline clinicians yet the association of these injuries with child abuse is often not r...

Using natural language processing to identify child maltreatment in health systems.

Child abuse & neglect
BACKGROUND: Rates of child maltreatment (CM) obtained from electronic health records are much lower than national child welfare prevalence rates indicate. There is a need to understand how CM is documented to improve reporting and surveillance.

Harnessing the Power of Machine Learning and Electronic Health Records to Support Child Abuse and Neglect Identification in Emergency Department Settings.

Studies in health technology and informatics
Emergency departments (EDs) are pivotal in detecting child abuse and neglect, but this task is often complex. Our study developed a machine learning model using structured and unstructured electronic health record (EHR) data to predict when children ...

Case reports unlocked: Harnessing large language models to advance research on child maltreatment.

Child abuse & neglect
BACKGROUND: Research on child protective services (CPS) is impeded by a lack of high-quality structured data. Crucial information on cases is often documented in case files, but only in narrative form. Researchers have applied automated language proc...

Using machine learning modeling to identify childhood abuse victims on the basis of personality inventory responses.

Journal of psychiatric research
Trauma is very common and associated with significant co-morbidity world-wide, particularly PTSD and frequently other mental health disorders. However, it can be challenging to identify victims of abuse as self-reports can be difficult to elicit due ...

Factors Associated with Abusive Head Trauma in Young Children Presenting to Emergency Medical Services Using a Large Language Model.

Prehospital emergency care
OBJECTIVES: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Larg...

Using supervised machine learning and ICD10 to identify non-accidental trauma in pediatric trauma patients in the Maryland Health Services Cost Review Commission dataset.

Child abuse & neglect
BACKGROUND: Identifying non-accidental trauma (NAT) in pediatric trauma patients is challenging. We developed a machine learning model that uses demographic characteristics and ICD10 codes to detect the first diagnosis of NAT.

Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females.

Scientific reports
Childhood abuse represents one of the most potent risk factors for the development of psychopathology during childhood, accounting for 30-60% of the risk for onset. While previous studies have separately associated reductions in gray matter volume (G...

Using explainable machine learning to investigate the relationship between childhood maltreatment, positive psychological traits, and CPTSD symptoms.

European journal of psychotraumatology
The functional impairment resulting from CPTSD symptoms is enduring and far-reaching. Existing research has found that CPTSD symptoms are closely associated with childhood maltreatment; however, researchers debate whether CPTSD symptoms are predomin...